Gen Report Date: 2024-07-18

Years: 2022, 2023

Channels: XStore Retail

Measures: Product Revenue($)

1 ) Product Revenue

1.1 Holidays and Sub Brands’ Promotions Percentage Model

1.1.1 Components

1.1.2 Cross Validation

Model: Add SUB_BRANDs’ Promotions as Regressors

RSquare: 0.7149872, MAPE: 0.6236067, MDAPE: 0.2895716

If removed the outliers (no. of outliers = 3) that Difference% > 100%, the performance as below:

RSquare: 0.8628255, MAPE: 0.256907, MDAPE: 0.2702861

These metrics are commonly used to evaluate the performance of a sales forecasting model.

  • RSquare (R²): 0.8628255

R-squared, also known as the coefficient of determination, measures the proportion of variance in the dependent variable (in this case, sales) that is predictable from the independent variable(s).

  • Range: 0 to 1

  • Interpretation: 0.8628255 means that approximately 86.28% of the variance in the sales data can be explained by the model.

  • Assessment: This is a relatively high R-squared value, indicating that the model fits the data well. Generally, an R-squared above 0.7 is considered good for sales forecasting.

  • MAPE (Mean Absolute Percentage Error): 0.256907

MAPE measures the average of the absolute percentage differences between the forecasted values and the actual values.

  • Calculation: Mean(|Actual - Forecast| / Actual) * 100

  • Interpretation: On average, the forecast is off by about 25.69% from the actual values.

  • Assessment: While there’s no universal standard, a MAPE of 25.69% suggests moderate accuracy. For sales forecasting, this might be considered acceptable, but there’s room for improvement.

  • MDAPE (Median Absolute Percentage Error): 0.2702861

MDAPE is similar to MAPE but uses the median instead of the mean, making it less sensitive to extreme errors.

  • Calculation: Median(|Actual - Forecast| / Actual) * 100
  • Interpretation: The median error is about 27.03% from the actual values.
  • Assessment: The MDAPE is slightly higher than the MAPE, which suggests that there might be some extreme errors pulling the MAPE down. The median error being around 27% indicates moderate accuracy.

Overall Assessment:

The model shows good explanatory power (high R-squared) but moderate predictive accuracy (MAPE and MDAPE around 25-27%). This suggests that while the model captures the general trends in the data well, there’s still a notable margin of error in its predictions. Depending on the specific requirements of your sales forecasting application, this level of accuracy might be acceptable, but there could be room for improvement, especially in reducing the percentage errors.

2 ) Appendix